KAI：用于数据高效关节物体操纵的运动感知接口
KAI: A Kinematic-Aware Interface for Data-Efficient Articulated Object Manipulation
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中文总结 AI 辅助
研究关节物体操纵中运动结构难学问题，提出运动感知关节接口KAI，通过嵌入先验提高样本效率，在多模拟任务中表现良好，能推广到复杂场景，视频共训练增强真实世界鲁棒性。
中文摘要 AI 辅助
关节物体操纵需要理解运动结构，仅从机器人演示中学习既困难又昂贵。我们引入了运动感知关节接口（KAI），这是一种结构化的中间表示，用于捕捉关节物体的运动结构。通过将可解释的几何和运动先验嵌入策略学习中，KAI提供了与关节运动的底层结构一致的强大归纳偏差。这种设计有效地提高了样本效率，在低数据情况下效果尤为显著：在六个模拟任务中，我们的方法平均成功率达到82.9%，仅使用一半的演示数据就能匹配或超过基线性能。我们的方法还能稳健地推广到未见背景和视觉干扰物，从单一干净的训练环境转移到杂乱的真实场景。KAI的动作无关设计还能通过与人类交互视频共同训练来增强真实世界的鲁棒性：在各种视觉干扰下，我们的视频共同训练方法平均成功率超过70%。
英文摘要
Articulated object manipulation requires an understanding of kinematic structure that is difficult and costly to learn from robot demonstrations alone. We introduce the Kinematic-Aware Articulation Interface (KAI), a structured intermediate representation that captures the kinematic structure of articulated objects. By embedding interpretable geometric and kinematic priors into policy learning, KAI provides a strong inductive bias aligned with the underlying structure of articulated motion. This design effectively improves sample efficiency, with gains particularly pronounced in low-data regimes: across six simulation tasks, our method achieves an average success rate of 82.9%, matching or surpassing baseline performance while using only half the demonstration data. Our method also exhibits robust generalization to unseen backgrounds and visual distractors, transferring from a single clean training environment to cluttered real-world scenes. KAI's action-agnostic design further enables co-training with human interaction videos to enhance real-world robustness: under diverse visual distractions, our method with video co-training achieves over 70% average success rate.
发表机构
- The Chinese University of Hong Kong（香港中文大学）
- Shanghai AI Laboratory（上海人工智能实验室）
- Shanghai Jiao Tong University（上海交通大学）
机构由 AI 辅助整理，请以论文原文为准。
